Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study.
Zahra MansouriYazdan SalimiMehdi AminiGhasem HajianfarMehrdad OveisiIsaac ShiriHabib ZaidiPublished in: Radiation oncology (London, England) (2024)
Our results demonstrated that clinical features, Dosiomics and fusion of dose and CT images by specific ML-FS models could predict the overall survival of HNC patients with acceptable accuracy. Besides, the performance of ML methods among the three different strategies was almost comparable.
Keyphrases
- contrast enhanced
- end stage renal disease
- computed tomography
- image quality
- ejection fraction
- newly diagnosed
- dual energy
- chronic kidney disease
- prognostic factors
- deep learning
- peritoneal dialysis
- magnetic resonance imaging
- positron emission tomography
- convolutional neural network
- machine learning
- magnetic resonance